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---
language: julia
contributors:
- ["Leah Hanson", "http://leahhanson.us"]
filename: learnjulia.jl
---
Julia is a new homoiconic functional language focused on technical computing.
While having the full power of homoiconic macros, first-class functions, and low-level control, Julia is as easy to learn and use as Python.
This is based on the current development version of Julia, as of October 18th, 2013.
```ruby
# Single line comments start with a hash.
####################################################
## 1. Primitive Datatypes and Operators
####################################################
# Everything in Julia is a expression.
# There are several basic types of numbers.
3 #=> 3 (Int64)
3.2 #=> 3.2 (Float64)
2 + 1im #=> 2 + 1im (Complex{Int64})
2//3 #=> 2//3 (Rational{Int64})
# All of the normal infix operators are available.
1 + 1 #=> 2
8 - 1 #=> 7
10 * 2 #=> 20
35 / 5 #=> 7.0
5 / 2 #=> 2.5 # dividing an Int by an Int always results in a Float
div(5, 2) #=> 2 # for a truncated result, use div
5 \ 35 #=> 7.0
2 ^ 2 #=> 4 # power, not bitwise xor
12 % 10 #=> 2
# Enforce precedence with parentheses
(1 + 3) * 2 #=> 8
# Bitwise Operators
~2 #=> -3 # bitwise not
3 & 5 #=> 1 # bitwise and
2 | 4 #=> 6 # bitwise or
2 $ 4 #=> 6 # bitwise xor
2 >>> 1 #=> 1 # logical shift right
2 >> 1 #=> 1 # arithmetic shift right
2 << 1 #=> 4 # logical/arithmetic shift left
# You can use the bits function to see the binary representation of a number.
bits(12345)
#=> "0000000000000000000000000000000000000000000000000011000000111001"
bits(12345.0)
#=> "0100000011001000000111001000000000000000000000000000000000000000"
# Boolean values are primitives
true
false
# Boolean operators
!true #=> false
!false #=> true
1 == 1 #=> true
2 == 1 #=> false
1 != 1 #=> false
2 != 1 #=> true
1 < 10 #=> true
1 > 10 #=> false
2 <= 2 #=> true
2 >= 2 #=> true
# Comparisons can be chained
1 < 2 < 3 #=> true
2 < 3 < 2 #=> false
# Strings are created with "
"This is a string."
# Character literals are written with '
'a'
# A string can be indexed like an array of characters
"This is a string"[1] #=> 'T' # Julia indexes from 1
# However, this is will not work well for UTF8 strings,
# so iterating over strings is recommended (map, for loops, etc).
# $ can be used for string interpolation:
"2 + 2 = $(2 + 2)" #=> "2 + 2 = 4"
# You can put any Julia expression inside the parenthesis.
# Another way to format strings is the printf macro.
@printf "%d is less than %f" 4.5 5.3 # 5 is less than 5.300000
####################################################
## 2. Variables and Collections
####################################################
# Printing is easy
println("I'm Julia. Nice to meet you!")
# You don't declare variables before assigning to them.
some_var = 5 #=> 5
some_var #=> 5
# Accessing a previously unassigned variable is an error
try
some_other_var #=> ERROR: some_other_var not defined
catch e
println(e)
end
# Variable names start with a letter.
# After that, you can use letters, digits, underscores, and exclamation points.
SomeOtherVar123! = 6 #=> 6
# You can also use unicode characters
☃ = 8 #=> 8
# These are especially handy for mathematical notation
2 * π #=> 6.283185307179586
# A note on naming conventions in Julia:
#
# * Names of variables are in lower case, with word separation indicated by
# underscores ('\_').
#
# * Names of Types begin with a capital letter and word separation is shown
# with CamelCase instead of underscores.
#
# * Names of functions and macros are in lower case, without underscores.
#
# * Functions that modify their inputs have names that end in !. These
# functions are sometimes called mutating functions or in-place functions.
# Arrays store a sequence of values indexed by integers 1 through n:
a = Int64[] #=> 0-element Int64 Array
# 1-dimensional array literals can be written with comma-separated values.
b = [4, 5, 6] #=> 3-element Int64 Array: [4, 5, 6]
b[1] #=> 4
b[end] #=> 6
# 2-dimentional arrays use space-separated values and semicolon-separated rows.
matrix = [1 2; 3 4] #=> 2x2 Int64 Array: [1 2; 3 4]
# Add stuff to the end of a list with push! and append!
push!(a,1) #=> [1]
push!(a,2) #=> [1,2]
push!(a,4) #=> [1,2,4]
push!(a,3) #=> [1,2,4,3]
append!(a,b) #=> [1,2,4,3,4,5,6]
# Remove from the end with pop
pop!(b) #=> 6 and b is now [4,5]
# Let's put it back
push!(b,6) # b is now [4,5,6] again.
a[1] #=> 1 # remember that Julia indexes from 1, not 0!
# end is a shorthand for the last index. It can be used in any
# indexing expression
a[end] #=> 6
# we also have shift and unshift
shift!(a) #=> 1 and a is now [2,4,3,4,5,6]
unshift!(a,7) #=> [7,2,4,3,4,5,6]
# Function names that end in exclamations points indicate that they modify
# their argument.
arr = [5,4,6] #=> 3-element Int64 Array: [5,4,6]
sort(arr) #=> [4,5,6]; arr is still [5,4,6]
sort!(arr) #=> [4,5,6]; arr is now [4,5,6]
# Looking out of bounds is a BoundsError
try
a[0] #=> ERROR: BoundsError() in getindex at array.jl:270
a[end+1] #=> ERROR: BoundsError() in getindex at array.jl:270
catch e
println(e)
end
# Errors list the line and file they came from, even if it's in the standard
# library. If you built Julia from source, you can look in the folder base
# inside the julia folder to find these files.
# You can initialize arrays from ranges
a = [1:5] #=> 5-element Int64 Array: [1,2,3,4,5]
# You can look at ranges with slice syntax.
a[1:3] #=> [1, 2, 3]
a[2:] #=> [2, 3, 4, 5]
a[2:end] #=> [2, 3, 4, 5]
# Remove elements from an array by index with splice!
arr = [3,4,5]
splice!(arr,2) #=> 4 ; arr is now [3,5]
# Concatenate lists with append!
b = [1,2,3]
append!(a,b) # Now a is [1, 2, 3, 4, 5, 1, 2, 3]
# Check for existence in a list with in
in(1, a) #=> true
# Examine the length with length
length(a) #=> 8
# Tuples are immutable.
tup = (1, 2, 3) #=> (1,2,3) # an (Int64,Int64,Int64) tuple.
tup[1] #=> 1
try:
tup[1] = 3 #=> ERROR: no method setindex!((Int64,Int64,Int64),Int64,Int64)
catch e
println(e)
end
# Many list functions also work on tuples
length(tup) #=> 3
tup[1:2] #=> (1,2)
in(2, tup) #=> true
# You can unpack tuples into variables
a, b, c = (1, 2, 3) #=> (1,2,3) # a is now 1, b is now 2 and c is now 3
# Tuples are created even if you leave out the parentheses
d, e, f = 4, 5, 6 #=> (4,5,6)
# A 1-element tuple is distinct from the value it contains
(1,) == 1 #=> false
(1) == 1 #=> true
# Look how easy it is to swap two values
e, d = d, e #=> (5,4) # d is now 5 and e is now 4
# Dictionaries store mappings
empty_dict = Dict() #=> Dict{Any,Any}()
# You can create a dictionary using a literal
filled_dict = ["one"=> 1, "two"=> 2, "three"=> 3]
# => Dict{ASCIIString,Int64}
# Look up values with []
filled_dict["one"] #=> 1
# Get all keys
keys(filled_dict)
#=> KeyIterator{Dict{ASCIIString,Int64}}(["three"=>3,"one"=>1,"two"=>2])
# Note - dictionary keys are not sorted or in the order you inserted them.
# Get all values
values(filled_dict)
#=> ValueIterator{Dict{ASCIIString,Int64}}(["three"=>3,"one"=>1,"two"=>2])
# Note - Same as above regarding key ordering.
# Check for existence of keys in a dictionary with in, haskey
in(("one", 1), filled_dict) #=> true
in(("two", 3), filled_dict) #=> false
haskey(filled_dict, "one") #=> true
haskey(filled_dict, 1) #=> false
# Trying to look up a non-existant key will raise an error
try
filled_dict["four"] #=> ERROR: key not found: four in getindex at dict.jl:489
catch e
println(e)
end
# Use the get method to avoid that error by providing a default value
# get(dictionary,key,default_value)
get(filled_dict,"one",4) #=> 1
get(filled_dict,"four",4) #=> 4
# Use Sets to represent collections of unordered, unique values
empty_set = Set() #=> Set{Any}()
# Initialize a set with values
filled_set = Set(1,2,2,3,4) #=> Set{Int64}(1,2,3,4)
# Add more values to a set
push!(filled_set,5) #=> Set{Int64}(5,4,2,3,1)
# Check if the values are in the set
in(2, filled_set) #=> true
in(10, filled_set) #=> false
# There are functions for set intersection, union, and difference.
other_set = Set(3, 4, 5, 6) #=> Set{Int64}(6,4,5,3)
intersect(filled_set, other_set) #=> Set{Int64}(3,4,5)
union(filled_set, other_set) #=> Set{Int64}(1,2,3,4,5,6)
setdiff(Set(1,2,3,4),Set(2,3,5)) #=> Set{Int64}(1,4)
####################################################
## 3. Control Flow
####################################################
# Let's make a variable
some_var = 5
# Here is an if statement. Indentation is not meaningful in Julia.
if some_var > 10
println("some_var is totally bigger than 10.")
elseif some_var < 10 # This elseif clause is optional.
println("some_var is smaller than 10.")
else # The else clause is optional too.
println("some_var is indeed 10.")
end
#=> prints "some var is smaller than 10"
# For loops iterate over iterables.
# Iterable types include Range, Array, Set, Dict, and String.
for animal=["dog", "cat", "mouse"]
println("$animal is a mammal")
# You can use $ to interpolate variables or expression into strings
end
# prints:
# dog is a mammal
# cat is a mammal
# mouse is a mammal
# You can use 'in' instead of '='.
for animal in ["dog", "cat", "mouse"]
println("$animal is a mammal")
end
# prints:
# dog is a mammal
# cat is a mammal
# mouse is a mammal
for a in ["dog"=>"mammal","cat"=>"mammal","mouse"=>"mammal"]
println("$(a[1]) is a $(a[2])")
end
# prints:
# dog is a mammal
# cat is a mammal
# mouse is a mammal
for (k,v) in ["dog"=>"mammal","cat"=>"mammal","mouse"=>"mammal"]
println("$k is a $v")
end
# prints:
# dog is a mammal
# cat is a mammal
# mouse is a mammal
# While loops loop while a condition is true
x = 0
while x < 4
println(x)
x += 1 # Shorthand for x = x + 1
end
# prints:
# 0
# 1
# 2
# 3
# Handle exceptions with a try/catch block
try
error("help")
catch e
println("caught it $e")
end
#=> caught it ErrorException("help")
####################################################
## 4. Functions
####################################################
# The keyword 'function' creates new functions
#function name(arglist)
# body...
#end
function add(x, y)
println("x is $x and y is $y")
# Functions return the value of their last statement
x + y
end
add(5, 6) #=> 11 after printing out "x is 5 and y is 6"
# You can define functions that take a variable number of
# positional arguments
function varargs(args...)
return args
# use the keyword return to return anywhere in the function
end
#=> varargs (generic function with 1 method)
varargs(1,2,3) #=> (1,2,3)
# The ... is called a splat.
# We just used it in a function definition.
# It can also be used in a fuction call,
# where it will splat an Array or Tuple's contents into the argument list.
Set([1,2,3]) #=> Set{Array{Int64,1}}([1,2,3]) # produces a Set of Arrays
Set([1,2,3]...) #=> Set{Int64}(1,2,3) # this is equivalent to Set(1,2,3)
x = (1,2,3) #=> (1,2,3)
Set(x) #=> Set{(Int64,Int64,Int64)}((1,2,3)) # a Set of Tuples
Set(x...) #=> Set{Int64}(2,3,1)
# You can define functions with optional positional arguments
function defaults(a,b,x=5,y=6)
return "$a $b and $x $y"
end
defaults('h','g') #=> "h g and 5 6"
defaults('h','g','j') #=> "h g and j 6"
defaults('h','g','j','k') #=> "h g and j k"
try
defaults('h') #=> ERROR: no method defaults(Char,)
defaults() #=> ERROR: no methods defaults()
catch e
println(e)
end
# You can define functions that take keyword arguments
function keyword_args(;k1=4,name2="hello") # note the ;
return ["k1"=>k1,"name2"=>name2]
end
keyword_args(name2="ness") #=> ["name2"=>"ness","k1"=>4]
keyword_args(k1="mine") #=> ["k1"=>"mine","name2"=>"hello"]
keyword_args() #=> ["name2"=>"hello","k1"=>4]
# You can combine all kinds of arguments in the same function
function all_the_args(normal_arg, optional_positional_arg=2; keyword_arg="foo")
println("normal arg: $normal_arg")
println("optional arg: $optional_positional_arg")
println("keyword arg: $keyword_arg")
end
all_the_args(1, 3, keyword_arg=4)
# prints:
# normal arg: 1
# optional arg: 3
# keyword arg: 4
# Julia has first class functions
function create_adder(x)
adder = function (y)
return x + y
end
return adder
end
# This is "stabby lambda syntax" for creating anonymous functions
(x -> x > 2)(3) #=> true
# This function is identical to create_adder implementation above.
function create_adder(x)
y -> x + y
end
# You can also name the internal function, if you want
function create_adder(x)
function adder(y)
x + y
end
adder
end
add_10 = create_adder(10)
add_10(3) #=> 13
# There are built-in higher order functions
map(add_10, [1,2,3]) #=> [11, 12, 13]
filter(x -> x > 5, [3, 4, 5, 6, 7]) #=> [6, 7]
# We can use list comprehensions for nicer maps
[add_10(i) for i=[1, 2, 3]] #=> [11, 12, 13]
[add_10(i) for i in [1, 2, 3]] #=> [11, 12, 13]
####################################################
## 5. Types
####################################################
# Julia has a type system.
# Every value has a type; variables do not have types themselves.
# You can use the `typeof` function to get the type of a value.
typeof(5) #=> Int64
# Types are first-class values
typeof(Int64) #=> DataType
typeof(DataType) #=> DataType
# DataType is the type that represents types, including itself.
# Types are used for documentation, optimizations, and dispatch.
# They are not statically checked.
# Users can define types
# They are like records or structs in other languages.
# New types are defined used the `type` keyword.
# type Name
# field::OptionalType
# ...
# end
type Tiger
taillength::Float64
coatcolor # not including a type annotation is the same as `::Any`
end
# The default constructor's arguments are the properties
# of the type, in the order they are listed in the definition
tigger = Tiger(3.5,"orange") #=> Tiger(3.5,"orange")
# The type doubles as the constructor function for values of that type
sherekhan = typeof(tigger)(5.6,"fire") #=> Tiger(5.6,"fire")
# These struct-style types are called concrete types
# They can be instantiated, but cannot have subtypes.
# The other kind of types is abstract types.
# abstract Name
abstract Cat # just a name and point in the type hierarchy
# Abstract types cannot be instantiated, but can have subtypes.
# For example, Number is an abstract type
subtypes(Number) #=> 6-element Array{Any,1}:
# Complex{Float16}
# Complex{Float32}
# Complex{Float64}
# Complex{T<:Real}
# ImaginaryUnit
# Real
subtypes(Cat) #=> 0-element Array{Any,1}
# Every type has a super type; use the `super` function to get it.
typeof(5) #=> Int64
super(Int64) #=> Signed
super(Signed) #=> Real
super(Real) #=> Number
super(Number) #=> Any
super(super(Signed)) #=> Number
super(Any) #=> Any
# All of these type, except for Int64, are abstract.
# <: is the subtyping operator
type Lion <: Cat # Lion is a subtype of Cat
mane_color
roar::String
end
# You can define more constructors for your type
# Just define a function of the same name as the type
# and call an existing constructor to get a value of the correct type
Lion(roar::String) = Lion("green",roar)
# This is an outer constructor because it's outside the type definition
type Panther <: Cat # Panther is also a subtype of Cat
eye_color
Panther() = new("green")
# Panthers will only have this constructor, and no default constructor.
end
# Using inner constructors, like Panther does, gives you control
# over how values of the type can be created.
# When possible, you should use outer constructors rather than inner ones.
####################################################
## 6. Multiple-Dispatch
####################################################
# In Julia, all named functions are generic functions
# This means that they are built up from many small methods
# Each constructor for Lion is a method of the generic function Lion.
# For a non-constructor example, let's make a function meow:
# Definitions for Lion, Panther, Tiger
function meow(animal::Lion)
animal.roar # access type properties using dot notation
end
function meow(animal::Panther)
"grrr"
end
function meow(animal::Tiger)
"rawwwr"
end
# Testing the meow function
meow(tigger) #=> "rawwr"
meow(Lion("brown","ROAAR")) #=> "ROAAR"
meow(Panther()) #=> "grrr"
# Review the local type hierarchy
issubtype(Tiger,Cat) #=> false
issubtype(Lion,Cat) #=> true
issubtype(Panther,Cat) #=> true
# Defining a function that takes Cats
function pet_cat(cat::Cat)
println("The cat says $(meow(cat))")
end
pet_cat(Lion("42")) #=> prints "The cat says 42"
try
pet_cat(tigger) #=> ERROR: no method pet_cat(Tiger,)
catch e
println(e)
end
# In OO languages, single dispatch is common;
# this means that the method is picked based on the type of the first argument.
# In Julia, all of the argument types contribute to selecting the best method.
# Let's define a function with more arguments, so we can see the difference
function fight(t::Tiger,c::Cat)
println("The $(t.coatcolor) tiger wins!")
end
#=> fight (generic function with 1 method)
fight(tigger,Panther()) #=> prints The orange tiger wins!
fight(tigger,Lion("ROAR")) #=> prints The orange tiger wins!
# Let's change the behavior when the Cat is specifically a Lion
fight(t::Tiger,l::Lion) = println("The $(l.mane_color)-maned lion wins!")
#=> fight (generic function with 2 methods)
fight(tigger,Panther()) #=> prints The orange tiger wins!
fight(tigger,Lion("ROAR")) #=> prints The green-maned lion wins!
# We don't need a Tiger in order to fight
fight(l::Lion,c::Cat) = println("The victorious cat says $(meow(c))")
#=> fight (generic function with 3 methods)
fight(Lion("balooga!"),Panther()) #=> prints The victorious cat says grrr
try
fight(Panther(),Lion("RAWR")) #=> ERROR: no method fight(Panther,Lion)
catch
end
# Also let the cat go first
fight(c::Cat,l::Lion) = println("The cat beats the Lion")
#=> Warning: New definition
# fight(Cat,Lion) at none:1
# is ambiguous with
# fight(Lion,Cat) at none:2.
# Make sure
# fight(Lion,Lion)
# is defined first.
#fight (generic function with 4 methods)
# This warning is because it's unclear which fight will be called in:
fight(Lion("RAR"),Lion("brown","rarrr")) #=> prints The victorious cat says rarrr
# The result may be different in other versions of Julia
fight(l::Lion,l2::Lion) = println("The lions come to a tie")
fight(Lion("RAR"),Lion("brown","rarrr")) #=> prints The lions come to a tie
# Under the hood
# You can take a look at the llvm and the assembly code generated.
square_area(l) = l * l # square_area (generic function with 1 method)
square_area(5) #25
# What happens when we feed square_area an integer?
code_native(square_area, (Int32,))
# .section __TEXT,__text,regular,pure_instructions
# Filename: none
# Source line: 1 # Prologue
# push RBP
# mov RBP, RSP
# Source line: 1
# movsxd RAX, EDI # Fetch l from memory?
# imul RAX, RAX # Square l and store the result in RAX
# pop RBP # Restore old base pointer
# ret # Result will still be in RAX
code_native(square_area, (Float32,))
# .section __TEXT,__text,regular,pure_instructions
# Filename: none
# Source line: 1
# push RBP
# mov RBP, RSP
# Source line: 1
# vmulss XMM0, XMM0, XMM0 # Scalar single precision multiply (AVX)
# pop RBP
# ret
code_native(square_area, (Float64,))
# .section __TEXT,__text,regular,pure_instructions
# Filename: none
# Source line: 1
# push RBP
# mov RBP, RSP
# Source line: 1
# vmulsd XMM0, XMM0, XMM0 # Scalar double precision multiply (AVX)
# pop RBP
# ret
#
# Note that julia will use floating point instructions if any of the
# arguements are floats.
# Let's calculate the area of a circle
circle_area(r) = pi * r * r # circle_area (generic function with 1 method)
circle_area(5) # 78.53981633974483
code_native(circle_area, (Int32,))
# .section __TEXT,__text,regular,pure_instructions
# Filename: none
# Source line: 1
# push RBP
# mov RBP, RSP
# Source line: 1
# vcvtsi2sd XMM0, XMM0, EDI # Load integer (r) from memory
# movabs RAX, 4593140240 # Load pi
# vmulsd XMM1, XMM0, QWORD PTR [RAX] # pi * r
# vmulsd XMM0, XMM0, XMM1 # (pi * r) * r
# pop RBP
# ret
#
code_native(circle_area, (Float64,))
# .section __TEXT,__text,regular,pure_instructions
# Filename: none
# Source line: 1
# push RBP
# mov RBP, RSP
# movabs RAX, 4593140496
# Source line: 1
# vmulsd XMM1, XMM0, QWORD PTR [RAX]
# vmulsd XMM0, XMM1, XMM0
# pop RBP
# ret
#
```
## Further Reading
You can get a lot more detail from [The Julia Manual](http://docs.julialang.org/en/latest/manual/)
The best place to get help with Julia is the (very friendly) [mailing list](https://groups.google.com/forum/#!forum/julia-users).
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